IEICE TRANSACTIONS on Information
Improving Fault Localization Using Conditional Variational Autoencoder
Summary :
Fault localization analyzes the runtime information of two classes of test cases (i.e., passing test cases and failing test cases) to identify suspicious statements potentially responsible for a failure. However, the failing test cases are always far fewer than passing test cases in reality, and the class imbalance problem will affect fault localization effectiveness. To address this issue, we propose a data augmentation approach using conditional variational auto-encoder to synthesize new failing test cases for FL. The experimental results show that our approach significantly improves six state-of-the-art fault localization techniques.
- Publication
- IEICE TRANSACTIONS on Information Vol.E105-D No.8 pp.1490-1494
- Publication Date
- 2022/08/01
- Publicized
- 2022/05/13
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2022EDL8024
- Type of Manuscript
- LETTER
- Category
- Software Engineering
Authors
Xianmei FANG
Hechi University
Xiaobo GAO
Hechi University
Yuting WANG
Hechi University
Zhouyu LIAO
Hechi University
Yue MA
Hechi University
Keyword
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Xianmei FANG, Xiaobo GAO, Yuting WANG, Zhouyu LIAO, Yue MA, "Improving Fault Localization Using Conditional Variational Autoencoder" in IEICE TRANSACTIONS on Information,
vol. E105-D, no. 8, pp. 1490-1494, August 2022, doi: 10.1587/transinf.2022EDL8024.
Abstract: Fault localization analyzes the runtime information of two classes of test cases (i.e., passing test cases and failing test cases) to identify suspicious statements potentially responsible for a failure. However, the failing test cases are always far fewer than passing test cases in reality, and the class imbalance problem will affect fault localization effectiveness. To address this issue, we propose a data augmentation approach using conditional variational auto-encoder to synthesize new failing test cases for FL. The experimental results show that our approach significantly improves six state-of-the-art fault localization techniques.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022EDL8024/_p
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@ARTICLE{e105-d_8_1490,
author={Xianmei FANG, Xiaobo GAO, Yuting WANG, Zhouyu LIAO, Yue MA, },
journal={IEICE TRANSACTIONS on Information},
title={Improving Fault Localization Using Conditional Variational Autoencoder},
year={2022},
volume={E105-D},
number={8},
pages={1490-1494},
abstract={Fault localization analyzes the runtime information of two classes of test cases (i.e., passing test cases and failing test cases) to identify suspicious statements potentially responsible for a failure. However, the failing test cases are always far fewer than passing test cases in reality, and the class imbalance problem will affect fault localization effectiveness. To address this issue, we propose a data augmentation approach using conditional variational auto-encoder to synthesize new failing test cases for FL. The experimental results show that our approach significantly improves six state-of-the-art fault localization techniques.},
keywords={},
doi={10.1587/transinf.2022EDL8024},
ISSN={1745-1361},
month={August},}
Copy
TY - JOUR
TI - Improving Fault Localization Using Conditional Variational Autoencoder
T2 - IEICE TRANSACTIONS on Information
SP - 1490
EP - 1494
AU - Xianmei FANG
AU - Xiaobo GAO
AU - Yuting WANG
AU - Zhouyu LIAO
AU - Yue MA
PY - 2022
DO - 10.1587/transinf.2022EDL8024
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2022
AB - Fault localization analyzes the runtime information of two classes of test cases (i.e., passing test cases and failing test cases) to identify suspicious statements potentially responsible for a failure. However, the failing test cases are always far fewer than passing test cases in reality, and the class imbalance problem will affect fault localization effectiveness. To address this issue, we propose a data augmentation approach using conditional variational auto-encoder to synthesize new failing test cases for FL. The experimental results show that our approach significantly improves six state-of-the-art fault localization techniques.
ER -
English
